:: Topics Covered
- Why Do we Need Statistics?
- Descriptive or Exploratory Data Analysis
- Overview and Goals
- Importance of Identifying the Type and Role of Variables in Studies
- Visualizing and Summarizing Data: The Concept of a Distribution
- Graphical Tools: histogram, Box-plot
- Numerical Tools: mean, median, standard deviation, standard error, etc.
- Exploring the relationship between two (2) variables
- Frequency tables for categorical variables
- Pearson's correlation coefficient for continuous variables
- Plots: Scatter plots, etc.
- Statistical inference or hypothesis testing
- Overview: What is statistical inference?
- Statistical Inference with Hypothesis Testing:
- Null and alternative hypotheses
- One-tailed vs. two-tailed tests
- Test statistics
- Observed significance level or "p-value"
- Statistical significance and decision rules
- Risk involved in hypothesis testing
- Risks or type I and II errors
- Confidence level of a test
- Power of test
- The importance of sample size calculations and the required input parameters to estimate a sample size
- Statistical inference with confidence Intervals: interpretation and usage
- Statistical Inference for a Single Sample or Group: Hypothesis Testing vs. Confidence Interval Approach
- Summary
:: Course ContentThis one-day workshop reviews the most important basic concepts in statistics. It begins with an overview of the role of statistics in a decision-making process. The types and roles of variables are discussed along with tools for characterizing and summarizing variables and for exploring the relationship between them.
Among the graphical tools presented for visualizing data are: the histogram, the box-plot and the scatter plot.
The course then turns to inferential statistics and describes the purpose, elements and scope of statistical tests: definition of a statistical test, what a p-value is, the risks associated with statistical tests and how these risks can be minimized.
Finally, we discuss confidence intervals and what is meant by "confidence". We cover what confidence intervals are, how to interpret them and the equivalence between confidence intervals and hypothesis testing.
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